8000 test: refactor test by JoanFM · Pull Request #3132 · jina-ai/serve · GitHub
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test: refactor test #3132

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Merged
merged 2 commits into from
Aug 10, 2021
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test: refactor test #3132

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test: refactor test
Aug 9, 2021
cf02424
test: refactor da/dam neural ops tests
alaeddine-13 Aug 10, 2021
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114 changes: 70 additions & 44 deletions tests/unit/types/arrays/test_neural_ops.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,5 @@
import copy
import os
import tempfile
from itertools import product

import numpy as np
import scipy.sparse as sp
Expand All @@ -13,7 +11,8 @@
from jina.math.dimensionality_reduction import PCA


def get_docs():
@pytest.fixture()
def doc_lists():
d1 = Document(embedding=np.array([0, 0, 0]))
d2 = Document(embedding=np.array([3, 0, 0]))
d3 = Document(embedding=np.array([1, 0, 0]))
Expand All @@ -28,39 +27,9 @@ def get_docs():
return [d1, d2, d3, d4], [d1_m, d2_m, d3_m, d4_m, d5_m]


def gen_docarrays(is_dam1=False, is_dam2=False, buffer_pool_size=1000):
def func():
D1, D2 = get_docs()
if is_dam1:
da1 = DocumentArrayMemmap(
tempfile.mkdtemp(), buffer_pool_size=buffer_pool_size
)
else:
da1 = DocumentArray()

if is_dam2:
da2 = DocumentArrayMemmap(
tempfile.mkdtemp(), buffer_pool_size=buffer_pool_size
)
else:
da2 = DocumentArray()
da1.extend(D1)
da2.extend(D2)
return da1, da2

return func


def gen_docarray_combinations():
return [
gen_docarrays(is_dam1, is_dam2)
for is_dam1, is_dam2 in product((True, False), (True, False))
] + [gen_docarrays(False, True, buffer_pool_size=3)]


@pytest.fixture
def docarrays_for_embedding_distance_computation():
D1, D2 = get_docs()
def docarrays_for_embedding_distance_computation(doc_lists):
D1, D2 = doc_lists
da1 = DocumentArray(D1)
da2 = DocumentArray(D2)
return da1, da2
Expand Down Expand Up @@ -89,12 +58,44 @@ def embeddings():
return np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]])


@pytest.mark.parametrize('gen_doc_arrays', gen_docarray_combinations())
def doc_lists_to_doc_arrays(
doc_lists, tmpdir, first_memmap, second_memmap, buffer_pool_size
):
doc_list1, doc_list2 = doc_lists

tmpdir1, tmpdir2 = tmpdir / '1', tmpdir / '2'

D1 = (
DocumentArray()
if not first_memmap
else DocumentArrayMemmap(tmpdir1, buffer_pool_size=buffer_pool_size)
)
D1.extend(doc_list1)
D2 = (
DocumentArray()
if not second_memmap
else DocumentArrayMemmap(tmpdir2, buffer_pool_size=buffer_pool_size)
)
D2.extend(doc_list2)
return D1, D2


@pytest.mark.parametrize('buffer_pool_size', [1000, 3])
@pytest.mark.parametrize('first_memmap', [True, False])
@pytest.mark.parametrize('second_memmap', [True, False])
@pytest.mark.parametrize(
'limit, batch_size', [(1, None), (2, None), (None, None), (1, 1), (1, 2), (2, 1)]
)
def test_matching_retrieves_correct_number(gen_doc_arrays, limit, batch_size):
D1, D2 = gen_doc_arrays()
def test_matching_retrieves_correct_number(
doc_lists, limit, batch_size, first_memmap, second_memmap, tmpdir, buffer_pool_size
):
D1, D2 = doc_lists_to_doc_arrays(
doc_lists,
tmpdir,
first_memmap,
second_memmap,
buffer_pool_size=buffer_pool_size,
)
D1.match(D2, metric='sqeuclidean', limit=limit, batch_size=batch_size)
for m in D1.get_attributes('matches'):
if limit is None:
Expand Down Expand Up @@ -186,6 +187,9 @@ def scipy_cdist_metric(X, Y, *args):
np.testing.assert_equal(distances, distances_scipy)


@pytest.mark.parametrize('buffer_pool_size', [1000, 3])
@pytest.mark.parametrize('first_memmap', [True, False])
@pytest.mark.parametrize('second_memmap', [True, False])
@pytest.mark.parametrize(
'normalization, metric',
[
Expand All @@ -198,14 +202,26 @@ def scipy_cdist_metric(X, Y, *args):
],
)
@pytest.mark.parametrize('use_scipy', [True, False])
@pytest.mark.parametrize('gen_doc_arrays', gen_docarray_combinations())
def test_matching_retrieves_closest_matches(
gen_doc_arrays, normalization, metric, use_scipy
doc_lists,
tmpdir,
normalization,
metric,
use_scipy,
first_memmap,
second_memmap,
buffer_pool_size,
):
"""
Tests if match.values are returned 'low to high' if normalization is True or 'high to low' otherwise
"""
D1, D2 = gen_doc_arrays()
D1, D2 = doc_lists_to_doc_arrays(
doc_lists,
tmpdir,
first_memmap,
second_memmap,
buffer_pool_size=buffer_pool_size,
)
D1.match(
D2, metric=metric, limit=3, normalization=normalization, use_scipy=use_scipy
)
Expand Down Expand Up @@ -284,12 +300,22 @@ def test_scipy_dist(
np.testing.assert_equal(values_docarray, values_docarraymemmap)


@pytest.mark.parametrize('gen_doc_arrays', gen_docarray_combinations())
def test_2arity_function(gen_doc_arrays):
@pytest.mark.parametrize('buffer_pool_size', [1000, 3])
@pytest.mark.parametrize('first_memmap', [True, False])
@pytest.mark.parametrize('second_memmap', [True, False])
def test_2arity_function(
first_memmap, second_memmap, doc_lists, tmpdir, buffer_pool_size
):
def dotp(x, y, *args):
return np.dot(x, np.transpose(y))

D1, D2 = gen_doc_arrays()
D1, D2 = doc_lists_to_doc_arrays(
doc_lists,
tmpdir,
first_memmap,
second_memmap,
buffer_pool_size=buffer_pool_size,
)
D1.match(D2, metric=dotp, use_scipy=True)

for d in D1:
Expand Down
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